Graph Neural Networks for Circuit Diagram Pattern Generation.

Graph neural networks (GNNs) have found majority of applications in multiple domains including physical science, molecular biology, etc. However, there is still scope of research in the application of GNNs in electrical domain. This research work tries to generate the circuit graphs that analyzes the GNNs. The end goal is to create a mechanism to iteratively predict the graph structure and thus complete a incomplete circuit diagram. The research work firstly tries to find out how well GNN can be used for predicting the missing node label or the missing node geometric features in the subset of the graph. Then, application of GNNs to anomaly detection problem is investigated. Next, GNN architecture is used to accurately predict the node label and approximate geometric features of the missing node in the circuit graph. Furthermore, a Graph Autoencoder (GAE) model is created and used for pruning the wrong edges in the circuit graph. The GNN model created for the purpose of anomaly detection problem gave around 90% accuracy. The GNN model used for missing node feature estimation gave around 89% accuracy to predict the correct label for missing node and it also performed effectively in approximating the geometric features of the missing node correctly. The link prediction model is able to classify the correct edges 92% of the times. Finally, a mechanism is provided that iteratively predicts graph structure using the anomaly detection model, node feature prediction model and link prediction model in a cycle.